1. Description of Prior Art
The usefulness of frequency spectrum analysis has long been known to the engineering community.
Electrical signals, such as speech, music, vibration, etc. coming from transducers, such as microphones and pick-ups, may be analyzed and examined by various techniques. Displaying these signals on an oscilloscope provides a represenation of amplitude versus time. It is very useful to obtain information about the frequency content of a signal. This may be done by using fixed frequency analyzers, such as filters attached to voltmeters and frequency selective voltmeters. However, these provide only a single point of frequecy information. The mathematics of spectrum analysis was worked out by the French mathematician Fourier, who proved that a periodic signal may be represented by a sum of sine waves, which include the fundamental and various harmonics. The amplitude of these harmonics represents a spectrum and may be graphically displayed to provide a visual indication of the frequency content of the signal, frequecy on one axis and amplitude on the other axis. The technique of spectrum analysis has been evolving over the years, becoming faster and more accurate. The earliest techniques took a sample of data, for example, a one second tape recording, and repeatedly analyzed it by mechanically or electrically moving a filter. The result would take many times the length of the sample to provide a complete set of data. For example, to analyze a 100 harmonic spectrum of data lasting one second, a moving filter analyzer would require 100 seconds. Obviously, if data is coming into the analyzer at a continuous rate, the operation of analysis takes so long that input data accumulates much faster than it can be analyzed. This is called a non real-time situation.
In most applications, it is critical that analysis be performed in real-time, that is, data is analzed as rapidly as it enters the system. A delay may be experienced between the input data and the output spectra, however, every piece of input data is being analyzed and outputted at the same rate. The techniques of real-time analysis have greatly benefited from computer technology and mathematical advances, such as the fast Fourier transform. The invention addresses itself to certain improvements in this field.
Early analog spectrum analysis techniques that generate a graph of amplitude versus frequency included moving filters, swept local oscillators, and multiple filter banks. All of these techniques suffer from a limited resolution on the frequency axis and poor dynamic range on the amplitude axis. Another limitation was their substantial size and weight. Over the years, such analyzers, which have occupied up to a cubic meter of volume, have been shrunk down in size but still are as big as a computer.
Some filter bank analyzers provide a constant percent bandwidth "third octave" analysis, which is very limited in frequency resolution. For the band from 20 Hz-20 kHz there are only 29 one-third octave bands.
In may applications, such as rotating machinery, vibration analysis, underwater sound, speech research, etc., much higher resolution is required. The fast-Fourier transform (FFT) technique has allowed computers to take over the function of analog filters and analog technique. The computing power required to do this is substantial and has, therefore, limited application of real-time spectrum analysis to bulky computers which are not portable and require substantial power. The prior art available high resolution, real-time analyzers required either a great sacrifice in dynamic range, bandwidth, resolution, etc., and are large, expensive, and difficult to use, oftentimes occupying an entire rack of equipment and costing from $10,000 to $30,000.
The prior art devices has a few fixed ranges for analysis bandwidth in a power of 2 sequence or a 1,2,5,10 progression. Each range needs additional filters and other hardware, increasing size and weight, as well as cost.
A spectrum analyzer using digital signal processing has a frequency range that is determined by the sample rate of the A/D converter. Changing this frequency range requires a corresponding change in the input anti-aliasing filter, which is used to limit the bandwidth of the input signal, according to the Nyquist criteria, as described in Mischa Schwartz, "Information Transmission, Modulation, and Noise," MaGraw Hill, 1959, p. 169-180, par. 4-5, 4-6. Prior art spectrum analyzers included a series of analog, switched capacitor, or other types of anti-aliasing filters for this purpose.